1. Field of the Invention
The present invention relates to a particle sampling method and a sensor fusion and filtering method, and more particularly, to a technique for obtaining an estimate and variance of each variable based on a constraint manifold.
2. Description of the Conventional Art
In general, sensor fusion refers to integrated processing for combining or fusing different types of sensor information into one representative information data.
FIG. 15 illustrates a pattern of general sensor fusion.
In FIG. 15, outputs X1 and X2 obtained from a first two sensors are fused into anew upper representative value X1,2. An output X3 obtained from a third sensor is fused with the fused node X1,2, thereby generating a new representative value X1,2,3 to be fused with an upper node.
As illustrated in FIG. 1, this sensor fusion and filtering method performs fusion and filtering 103 through two stages: prediction 101 using an equation for a system model and observation 102 using an equation for observation. Conventional filters using the sensor fusion and filtering method include a Kalman filter, an extended Kalman filter, an unscented Kalman filter, a particle filter, and so on.
Among these filters, the Kalman filter for basic sensor fusion and filtering is a linear filter suitable for a Gaussian error model, but it has a problem when it comes to non-linear system processing. For the purpose of non-linear system processing, an extended Kalman filter has been developed. However, the extended Kalman filter has a drawback that an error is caused by linearization, and thus its performance can vary according to non-linearity. As an alternative to the extended Kalman filter, an unscented Kalman filter, which treats non-linear and Gaussian error model systems, has been developed. The unscented Kalman filter has been actively studied up to present, but it has a problem in that its performance can be lowered due to inaccurate Gaussian approximation.
In order to cope with the problems of these Kalman filters, a particle filter has been developed. The particle filter has the advantage of employing a method of indicating results fused by particles having various weights, and thus it can be applied to fusion and filtering of a system having non-linear and arbitrary error modes as well as ambiguous information. However, the particle filter creates a problem in real-time processing due to failure and long processing time resulting from inaccurate prediction of particles.
The following references are related to sensor fusion and filtering:
[1] M. S. Grewal and A. P. Andrews, “Kalman Filtering,” 2nd ed. New York: Wiley, 2001.
[2] H. W. Sorenson, “Recursive Estimation for Nonlinear Dynamic Systems,” New York: Marcel Dekker, 1988.
[3] A. H. Jazwinski “Stochastic Processes and Filtering Theory,” New York: Academic, 1970.
[4] T. Ghirmai N. F. Bugallo, J. Miguez and P. P. Djuri, “A Novel Particle Filtering Approach to Blind Symbol Detection and Timing Estimation,” IEEE 58th Vehicular Technology Conference (VTC2003), vol. 2, pp. 1147-1151, October 2003.
[5] D. Crisan and A. Doucet, “A Survey of Convergence Results on Particle Filtering Methods for Practitioners,” IEEE Transactions on Signal Processing, vol. 50, pp. 736-746, March 2002.
[6] J. H. Kotecha and P. M. Djuric´, “Sequential Monte Carlo Sampling Detector for Rayleigh Fast-fading Channels,” in Proc. Int. Conf. Acoust., Speech, Signal Process., 2000.
[7] A. Doucet, S. Godsill, and C. Andrieu, “On Sequential Monte Carlo Sampling Methods for Bayesian Filtering,” Statist. Comput., vol. 10, no. 3, pp. 197-208, 2000.